Big data-based cadre assessment system

Abstract

The invention relates to the technical field of cadre assessment, in particular to a cadre assessment system based on big data, and the cadre assessment system based on the big data comprises a data integrity verification module, an intelligent weight adjustment module, a communication efficiency analysis module and a result integration and feedback module. According to the method, the security and accuracy of cadre assessment are enhanced through data processing, the security and traceability of assessment information are ensured through data integrity state verification, the processing efficiency and accuracy of assessment data are optimized, assessment index weights are dynamically adjusted to enable assessment and organization targets to be more closely connected, the scientificity and target guidance of assessment are enhanced, and the assessment efficiency is improved. The deep analysis of the communication efficiency directly improves the accurate evaluation of the potential and leadership of the cadre by accurately capturing the key information in the cadre communication, so that the cadre selection and training are more accurate, the efficiency and effect of making a personnel strategy by a management layer are greatly improved, and the overall performance of the cadre team and the management efficiency of the organization are improved at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of cadre assessment, and in particular to a cadre assessment system based on big data. Background Art

[0002] The field of cadre assessment technology involves systematic assessment methods and tools for monitoring and evaluating the work performance, professional behavior and contribution of civil servants or corporate cadres to organizational goals, combining various principles and practices of human resource management, such as performance management, goal setting and feedback, and career development planning. With the development of information technology, especially the application of big data and artificial intelligence, the field of cadre assessment technology has changed from traditional manual recording and evaluation methods to more automated, quantitative and systematic modern management tools, which not only improves the efficiency and transparency of assessment, but also makes the selection, training and promotion process of cadres more fair and accurate.

[0003] Among them, the cadre assessment system based on big data technology is a system that uses big data analysis and processing technology to evaluate the work performance and potential capabilities of cadres. Its main purpose is to provide a scientific and objective cadre evaluation mechanism. By collecting and analyzing the work data of cadres (such as project completion, leadership performance, team interaction, etc.), it helps management make more accurate personnel decisions, such as promotion, training needs analysis, and performance improvement, which helps to improve the organization's management efficiency and the overall quality of the cadre team.

[0004] Existing technologies have significant deficiencies in automation and real-time data processing. Reliance on manual operations results in slow and error-prone data collection and analysis processes, which limits the timely updating and accuracy of assessment results. Lack of effective control over data security and traceability leads to data leakage or tampering, affecting the fairness and transparency of assessments, causing delays in personnel decisions, and inadequate identification of cadres' potential and performance, affecting the overall operational efficiency of the organization and the rational allocation of human resources. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a cadre assessment system based on big data.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: The cadre assessment system based on big data includes:

[0007] The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records the timestamp and digital signature based on the hash value calculation results to verify the security and traceability of the data and obtain the data integrity status record;

[0008] The intelligent weight adjustment module extracts the assessment indicators based on the data integrity status record, calculates the correlation between the assessment indicators and the organizational goals, dynamically adjusts the weight settings according to the correlation calculation results, determines the consistency between the assessment standards and the organizational goals, and obtains the adjusted assessment indicator weights;

[0009] The communication effectiveness analysis module simultaneously analyzes the communication texts of the cadres based on the adjusted assessment index weights, evaluates the emotional tendency and topic logic, extracts key information based on the evaluation results, analyzes the communication effectiveness of the cadres, and generates communication effectiveness evaluation results;

[0010] The result integration and feedback module conducts a comprehensive evaluation of the cadres based on the communication effectiveness evaluation results, automatically pushes assessment information to the cadres, and establishes comprehensive assessment and feedback results.

[0011] As a further solution of the present invention, the steps for calculating the hash value of the data node are:

[0012] Extract cadres' annual performance data, project contributions and team feedback information, unify data formats, confirm data continuity, and generate original assessment data sets;

[0013] Based on the original assessment data set, each piece of data is encrypted and converted to verify the security and integrity of the data during processing and transmission, and the encrypted assessment data is obtained;

[0014] The encrypted assessment data is analyzed using the formula:

[0015] H(T)=H(H(L1)⊕H(L2))

[0016] Calculate the top-level hash value H(T) of the data set to obtain the hash value of the data node, where ⊕ represents the binary XOR operation, H represents the hash function, and L1 and L2 represent the hash values ​​of any two adjacent data nodes.

[0017] As a further solution of the present invention, the step of obtaining the data integrity status record is:

[0018] Based on the hash value of the data node, the current timestamp is synchronously recorded and digitally signed to confirm the immutability of the data and generate signature data with a timestamp;

[0019] Perform integrity verification on the signature data with the timestamp, use the public key to confirm that the data has not been tampered with from the time of signing to the present, verify the authenticity and integrity of the data, and obtain the verified signature data;

[0020] According to the verified signature data, the signature verification time and status of each data node are recorded, the integrity and security status of the data are confirmed, and a data integrity status record is generated.

[0021] As a further solution of the present invention, the calculation step of the correlation degree is:

[0022] Based on the data integrity status record, perform data query and screening, extract key assessment indicators, and generate preliminary screening results of assessment indicators;

[0023] According to the preliminary screening results of the assessment indicators, the occurrence frequency and importance of the assessment indicators are analyzed, and the first-order correlation coefficient between each assessment indicator and the organizational goal is calculated to obtain the preliminary correlation analysis results between the assessment indicators and the organizational goal;

[0024] According to the preliminary correlation analysis results between the assessment indicators and organizational goals, the formula is adopted:

[0025]

[0026] Calculate the correlation R between the i-th assessment indicator and the organizational goal i , get the correlation analysis record between the assessment indicators and the organizational goals, where p ij is the first-order correlation coefficient of assessment indicator i to organizational goal j, o j represents the operational complexity of organizational goal j, and n is the total number of organizational goals.

[0027] As a further solution of the present invention, the steps for obtaining the adjusted assessment index weights are:

[0028] Based on the correlation analysis records between the assessment indicators and the organizational goals, preliminarily adjust the weight of each assessment indicator to maximize the probability of achieving the organizational goals and generate a preliminarily adjusted weight configuration;

[0029] Apply the weight configuration after the preliminary adjustment to conduct sensitivity analysis, confirm the impact of the new weight setting on the achievement of the target through simulation analysis, compare the performance impact before and after the indicator weight adjustment, and generate the weight adjustment impact analysis results;

[0030] Based on the results of the weight adjustment impact analysis, the effects of the weight adjustments of all assessment indicators are revealed, aligned with the organizational goals, and the adjusted assessment indicator weights are generated.

[0031] As a further solution of the present invention, the steps of evaluating the sentiment tendency and topic logic are:

[0032] Using the adjusted assessment indicator weights, comprehensively analyze the cadre communication texts, extract key words and phrases from the texts, and generate preliminary text analysis results;

[0033] Based on the preliminary text analysis results, sentiment analysis is performed using the formula:

[0034]

[0035] Calculate the sentiment tendency score of cadre communication texts and generate sentiment tendency evaluation results, where Q represents the positive sentiment score and N represents the negative sentiment score;

[0036] Combined with the sentiment tendency assessment results, text logic analysis is conducted to evaluate the thematic logic of the cadre communication text, confirm the consistency of the content with organizational goals, and generate comprehensive assessment results.

[0037] As a further solution of the present invention, the steps of obtaining the communication effectiveness evaluation result are:

[0038] Based on the comprehensive evaluation results, extract key information, identify key factors affecting communication effectiveness, and generate key factor analysis results;

[0039] According to the results of the key factor analysis, the communication effectiveness of cadres is analyzed using the formula:

[0040]

[0041] Calculate the communication effectiveness index E eff , generate the results of cadre communication effectiveness analysis, where I i represents the standardized value of key information, and m is the number of key information;

[0042] Based on the results of the cadre communication effectiveness analysis, combined with the communication frequency and the frequency of occurrence of key information, and compared with the average performance within the organization, the strengths and weaknesses of cadres in communication are identified, and communication effectiveness evaluation results are generated.

[0043] As a further solution of the present invention, the steps for obtaining the comprehensive assessment and feedback results are:

[0044] Based on the communication effectiveness evaluation results, combined with work performance and leadership performance, the formula is used:

[0045]

[0046] Calculate the cadre comprehensive evaluation index CI to obtain the cadre comprehensive evaluation record, where CEI represents the communication effectiveness index, WP represents the work performance score, and LP represents the leadership performance score;

[0047] According to the comprehensive evaluation records of the cadres, a grading standard is set. If the comprehensive evaluation index of a certain department exceeds the set standard, it is marked as excellent and the cadre evaluation grade is obtained;

[0048] Based on the comprehensive evaluation records of the cadres and the evaluation levels of the cadres, they are uploaded to the personal information platform, a feedback mechanism is established, training and improvement suggestions are triggered, and comprehensive assessment and feedback results are generated.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, the security and accuracy of cadre assessment are enhanced through data processing, data integrity status verification ensures the security and traceability of assessment information, the processing efficiency and accuracy of assessment data are optimized, and the assessment index weights are dynamically adjusted to make the assessment more closely linked to organizational goals, thereby enhancing the scientific nature and goal orientation of the assessment. The in-depth analysis of communication effectiveness directly improves the accurate assessment of cadre potential and leadership by accurately capturing key information in cadre communication, making cadre selection and training more accurate, greatly improving the efficiency and effectiveness of management in formulating personnel strategies, and at the same time improving the overall performance of the cadre team and the management efficiency of the organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 This is a flow chart for calculating the hash value of a data node of the present invention;

[0053] Figure 3 A flowchart for obtaining data integrity status records of the present invention;

[0054] Figure 4 is a flow chart for calculating the correlation degree of the present invention;

[0055] Figure 5 This is a flow chart for obtaining the adjusted assessment index weights of the present invention;

[0056] Figure 6 It is a flow chart for evaluating the emotional tendency and theme logic of the present invention;

[0057] Figure 7 A flow chart for obtaining the communication effectiveness evaluation results of the present invention;

[0058] Figure 8 The figure is a flow chart for obtaining the comprehensive assessment and feedback results of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 , the cadre assessment system based on big data includes:

[0062] The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records the timestamp and digital signature based on the hash value calculation results to verify the security and traceability of the data and obtain the data integrity status record;

[0063] The intelligent weight adjustment module extracts the assessment indicators based on the data integrity status records, calculates the correlation between the assessment indicators and the organizational goals, dynamically adjusts the weight settings according to the correlation calculation results, determines the consistency between the assessment standards and the organizational goals, and obtains the adjusted assessment indicator weights;

[0064] The communication effectiveness analysis module analyzes the cadres’ communication texts based on the adjusted assessment index weights, evaluates the emotional tendency and topic logic, extracts key information based on the evaluation results, analyzes the cadres’ communication effectiveness, and generates communication effectiveness evaluation results;

[0065] The result integration and feedback module conducts a comprehensive evaluation of cadres based on the communication effectiveness evaluation results, automatically pushes assessment information to cadres, and establishes comprehensive assessment and feedback results.

[0066] Data integrity status records include hash verification results, time stamp results and security signatures; adjusted assessment indicator weights include weight adjustment records, strategy consistency analysis results and target correlation calculation results; communication effectiveness evaluation results include sentiment analysis results and logical coherence analysis results; comprehensive assessment and feedback results include assessment evaluation system, feedback notification information and assessment report output records.

[0067] See also Figure 2 , the calculation steps of the data node hash value are:

[0068] Extract cadres' annual performance data, project contributions and team feedback information, unify data formats, confirm data continuity, and generate original assessment data sets;

[0069] Export the cadre assessment data from the performance management system. This data includes, but is not limited to, annual performance scores, project contribution records, and peer feedback information among colleagues. Extract these data through a standardized interface and ensure the integrity and accuracy of the data through real-time data verification. During the data extraction process, specific operations include verifying the data format, checking for data missingness, and confirming the continuity of time-series data. The purpose of this stage is to ensure that the collected dataset can reflect the real and comprehensive performance situation, thus laying a solid foundation for subsequent data processing. The generated original assessment dataset will be used for further data analysis and encryption processing.

[0070] Based on the original assessment dataset, perform encryption conversion on each piece of data to verify the security and integrity of the data during processing and transmission, obtaining the encrypted assessment data;

[0071] Perform encryption processing on the original data obtained from the performance management system to ensure its security and privacy during storage and transmission. The specific encryption operations include applying a high-strength encryption algorithm to transform the data and generate an irreversible encrypted hash value. This encryption process also includes uniquely marking each piece of data for subsequent data tracking and auditing. The encrypted data is transmitted to the central database through a secure channel, ensuring the non-tamperability and non-identifiability of the data during transmission. The generated encrypted assessment data provides double protection for data integrity and security.

[0072] For the encrypted assessment data, use the formula:

[0073]

[0074] Calculate the top-level hash value H(T) of the dataset to obtain the data node hash value, where represents the binary XOR operation, H represents the hash function, and L1 and L2 represent the hash values of any two adjacent data nodes;

[0075] H represents the hash function, which is used to convert data into a fixed-size value to ensure security and consistency. represents the binary XOR operation, which is used to merge the information of two hash values to enhance data security. L1 and L2 respectively represent the hash values of any two adjacent data nodes. If the value of L1 is 'abc' and the value of L2 is 'def′, after being processed by the hash function, H( ′ abc ′ ) = 1a2b3c and H( ′ def ′)=4d5e6f, then the value after XOR operation is 5f7d0b. Further application of hash function yields H(5f7d0b)=7g8h9i, which is the hash value of top-level node T, and then the hash value of data node. This result shows that through hash and XOR operation, the security features of two data nodes can be combined to improve the security of the overall data structure.

[0076] See also Figure 3 , the steps to obtain the data integrity status record are:

[0077] Based on the hash value of the data node, the current timestamp is synchronously recorded and digitally signed to confirm the data is tamper-proof and generate signature data with a timestamp;

[0078] Using the Advanced Encryption Standard (AES) and digital signature technology, the data node hash value is automatically obtained from the previous step and combined with the current system timestamp. Using the private key, the system encrypts the hash value of the data node with the timestamp to form an irreversible digital signature, which ensures that the time and content of each data record cannot be tampered with. The process not only enhances the security protection layer of the data, but also ensures that the source and status of the data can be accurately traced at any time. The generated timestamp signature data provides key information for subsequent audits and verifications, which is an important step in maintaining data integrity and security.

[0079] Perform integrity verification on the signature data with timestamp, use the public key to confirm that the data has not been tampered with from the time of signing to the present, verify the authenticity and integrity of the data, and obtain the verified signature data;

[0080] The timestamp signature data generated above is verified using the public key encryption framework to ensure that it has not been tampered with since the data was signed. The verification operation uses RSA public key decryption technology to confirm the integrity of the data by comparing the decrypted hash value with the stored original hash value. The rigor of this operation not only ensures the security of data transmission and storage, but also ensures that the entire data processing process meets high security and compliance standards, strengthens the trust mechanism, ensures that corporate data is protected at all times, and reduces potential security risks.

[0081] According to the verified signature data, record the signature verification time and status of each data node, confirm the integrity and security status of the data, and generate a data integrity status record;

[0082] After successfully verifying the authenticity and integrity of the data, the verification time and status of each data node are automatically recorded. The recording operation is performed through the log management system, which records the verification time, operator, verification status and results of each data node in detail, forming a detailed data status log. It not only provides a clear historical record for each access and change of the data, but also ensures the integrity and traceability of the data. The generated data integrity status record provides strong data support for enterprises, so that when conducting data audits, compliance checks or security analysis, these detailed records can be relied on to ensure the correctness and legality of each step of the operation.

[0083] See also Figure 4 , the calculation steps of correlation are:

[0084] Based on the data integrity status records, perform data query and screening, extract key assessment indicators, and generate preliminary screening results of assessment indicators;

[0085] Based on the data integrity status record, the threshold of the data integrity score is set to 0.8, and the data is filtered using SQL query statements such as:

[0086] SELECT*FROMdata_recordsWHEREintegrity_score>=0.8

[0087] During the process, considering that the integrity standards of different data types such as text, numbers and timestamps may be different, different scoring criteria are set for each type. Then, the qualified data records are evaluated for quality, including checking for missing data, outliers and consistency errors. Through meticulous steps, it can be ensured that the screened data is of high quality and reliable. After that, high-quality data records are used to extract key assessment indicators, such as sales, customer satisfaction, etc., which will be used for further data analysis and decision support, and generate preliminary screening results for assessment indicators.

[0088] According to the preliminary screening results of the assessment indicators, analyze the frequency and importance of the assessment indicators, calculate the first-order correlation coefficient between each assessment indicator and the organizational goal, and obtain the preliminary correlation analysis results between the assessment indicators and the organizational goals;

[0089] After obtaining the initial screening results of the assessment indicators, an in-depth analysis is conducted on the screened assessment indicators. First, the frequency and importance of each assessment indicator in the historical data are calculated, and basic statistical descriptions such as frequency analysis, mean and standard deviation are used. Then, Pearson correlation analysis is applied to determine the strength of the linear relationship between each assessment indicator and the organizational goals. For example, the calculated correlation coefficient can be used to determine the direct relationship between a certain assessment indicator and the organizational success rate. In addition, Spearman rank correlation analysis will be considered to evaluate the correlation of non-parametric data. Statistical analysis not only helps identify indicators that are highly correlated with organizational goals, but also reveals those assessment indicators that may need to be re-evaluated or adjusted. The final correlation analysis results will directly support the organization's decision-making at the strategic level and improve the data-driven and accuracy of decision-making.

[0090] According to the preliminary correlation analysis results between the assessment indicators and organizational goals, the formula is used:

[0091]

[0092] Calculate the correlation R between the i-th assessment indicator and the organizational goal i , get the correlation analysis record between the assessment indicators and the organizational goals, where p ij is the first-order correlation coefficient of assessment indicator i to organizational goal j, o j represents the operational complexity of organizational goal j, and n is the total number of organizational goals;

[0093] Collect the correlation coefficient p between each assessment indicator i and each organizational goal j ij , assuming there is a correlation coefficient matrix about the goals, then, evaluate the operational complexity of each organizational goal o j For example, complexity can be quantified by the difficulty of implementing past projects and the resources required. Finally, the square root and absolute value operations in the formula are used to combine the factors and calculate the comprehensive correlation. For example, if there are three organizational goals, the correlation coefficients are 0.8, 0.5, and 0.9, and the operational complexities are 1.2, 1.5, and 1.1, respectively, the calculation process is:

[0094]

[0095] The results show that the comprehensive correlation between the assessment indicator i and the organizational goal is 0.61, indicating that the correlation between the indicator i and the organizational goal is moderate, and this value can be used to evaluate and optimize the strategy for achieving the organizational goal.

[0096] See also Figure 5 , the steps to obtain the adjusted assessment index weights are:

[0097] Based on the correlation analysis records between the assessment indicators and the organizational goals, the weight of each assessment indicator is preliminarily adjusted to maximize the probability of achieving the organizational goals and generate the preliminarily adjusted weight configuration;

[0098] When adjusting the weights of assessment indicators, it is first necessary to determine the basic importance of each indicator based on the report on the correlation with organizational goals, and conduct a detailed analysis of the data in the report to ensure that the weight of each indicator reflects its actual impact on organizational goals. Next, a linear programming model is used to optimize the weights. This model dynamically adjusts the weights according to the specific values ​​of the correlation to improve overall organizational efficiency. During the model operation, the correlation data is converted into the main input for weight adjustment. The conversion involves data standardization and the application of optimization algorithms, such as using Lagrangian relaxation technology to solve possible nonlinear problems, ensuring that the weight adjustment is not only based on actual data, but also has operational flexibility and accuracy to generate a preliminary adjusted weight configuration.

[0099] Apply the weight configuration after the initial adjustment to conduct sensitivity analysis, confirm the impact of the new weight setting on the achievement of the target through simulation analysis, compare the performance impact before and after the indicator weight adjustment, and generate the analysis results of the weight adjustment impact;

[0100] Apply the weight configuration after the preliminary adjustment to evaluate the implementation effect of the new weight configuration, including simulation comparison of organizational performance before and after the weight adjustment, and use sensitivity analysis to explore the specific impact of weight changes on performance. The analysis methods include but are not limited to multi-scenario simulation. These scenario simulations are based on expected performance data under different weight configurations to evaluate the effectiveness of weight adjustment. Through this method, we can clearly see the role of weight adjustment in promoting the achievement of organizational goals. During the analysis process, statistical tools such as SPSS or R will also be used for data processing to ensure the accuracy and reliability of the analysis results, so as to determine the consistency between the assessment standards and organizational goals.

[0101] Based on the results of the weight adjustment impact analysis, the effects of all assessment indicator weight adjustments are revealed, aligned with organizational goals, and the adjusted assessment indicator weights are generated;

[0102] In the above content, the formula is used:

[0103] Z=∑(w i ×P i )

[0104] Calculate the comprehensive adjusted assessment index weight Z, w i is the adjusted weight of indicator i, P iis the contribution of indicator i to the organizational goal. The weights and contributions of all assessment indicators are integrated by weighted summation. There are three assessment indicators, whose weights are adjusted to 0.3, 0.4, and 0.3 respectively, and their contributions to the organizational goal are 50, 60, and 70 respectively. The calculation is as follows:

[0105] Z = 0.3 × 50 + 0.4 × 60 + 0.3 × 70

[0106] =15+24+21=60

[0107] The results show that the comprehensive weight is 60, which means that after the weight adjustment, the entire assessment system supports the organizational goals more balanced and effectively, and further guides how to adjust the strategy or maintain the current weight configuration to maximize the achievement of organizational goals.

[0108] See also Figure 6 , the evaluation steps of sentiment tendency and topic logic are:

[0109] Use the adjusted assessment index weights to conduct a comprehensive analysis of cadre communication texts, extract key words and phrases from the texts, and generate preliminary text analysis results;

[0110] The adjusted assessment indicator weights are used to conduct a comprehensive analysis of the cadres' communication texts, and natural language processing technology is used to extract keywords and phrases from a large number of communication records to ensure that the keywords accurately capture the themes and emotional tendencies in the communication texts. This analysis is based on a deep learning model. The model training uses a large corpus to ensure that it can understand industry-specific terminology and expressions. The analysis tool determines the key elements of the text through word frequency, part-of-speech tagging, and semantic relevance analysis. The preliminary text analysis results include a quantitative assessment of the cadres' communication efficiency and style. The extraction of these keywords and phrases provides basic data for subsequent sentiment analysis and provides initial input data for the accurate assessment of cadres' communication texts, thereby constructing a basic emotional framework for communication texts.

[0111] Based on the preliminary text analysis results, sentiment analysis is performed using the formula:

[0112]

[0113] Calculate the sentiment tendency score E of the cadre communication text and generate the sentiment tendency evaluation result, where Q represents the positive sentiment score and N represents the negative sentiment score;

[0114] Through text analysis, it is determined that Q = 20 is the positive expression score and N = 10 is the negative expression score, and the calculation is as follows:

[0115]

[0116] The result shows that 0.471 is close to 0.5, and the cadre’s communication text has a moderately positive sentiment tendency.

[0117] Combined with the results of the sentiment tendency assessment, text logic analysis is conducted to evaluate the topic logic of the cadre communication text, confirm the consistency of the content with the organizational goals, and generate comprehensive assessment results;

[0118] Combined with the sentiment tendency assessment results obtained previously, the text logic analysis model is used to further evaluate the thematic logic of the communication text. By analyzing the sentence structure, logical connectors and context depth in the text, the coherence and logical consistency of the information are evaluated to ensure the alignment of the communication content with the organizational goals. This analysis is performed through advanced text analysis tools. These tools use machine learning algorithms to analyze the structure and content of the text, detect the logical connection and structural integration between sentences, and thus evaluate the logic of the text. The evaluation model scores the contribution and relevance of each piece of information. The comprehensive evaluation results not only demonstrate the logical clarity of the cadres' communication, but also reflect their ability to support responsibilities and organizational goals, thereby generating an indicator that reflects the degree of fit between communication effectiveness and organizational goals.

[0119] See also Figure 7 , the steps to obtain the communication effectiveness evaluation results are:

[0120] Based on the comprehensive evaluation results, extract key information, identify key factors affecting communication effectiveness, and generate key factor analysis results;

[0121] First, the evaluation results of sentiment tendency and topic logic are analyzed using data clustering to extract key information that affects the effectiveness of cadres' communication. This process begins with a comprehensive consideration of keywords and phrases in the communication text, analyzing the frequency of occurrence and contextual relevance. In this way, the model is able to identify the words and expressions that best represent the communication style and efficiency. The data is then imported into a clustering algorithm, which groups the data according to the similarity of the information to identify the topics that play a key role in communication. Each cluster represents a group of keywords that share the same or similar characteristics and together constitute the factors that affect the effectiveness of cadres' communication. This stage of analysis is not only based on semantic analysis, but also combines the sentiment tendency of vocabulary to ensure a comprehensive understanding of the cadres' communication style from every perspective. Through this detailed data processing and analysis, a key information extraction result is generated that accurately reflects the main factors affecting the effectiveness of cadres' communication.

[0122] According to the key factors analysis results, the communication effectiveness of cadres is analyzed using the formula:

[0123]

[0124] Calculate the communication effectiveness index E eff, generate the results of cadre communication effectiveness analysis, where I i represents the standardized value of key information, and m is the number of key information;

[0125] I i The values ​​of are 5, 3, and 7 respectively, and the total number m is 3, then the calculation process is as follows:

[0126]

[0127] The results show that based on the extracted key information, the cadres’ communication effectiveness index is 5.26, indicating relatively high communication effectiveness.

[0128] Based on the results of the cadre communication effectiveness analysis, the frequency of communication and the frequency of occurrence of key information are compared with the average performance within the organization to identify the strengths and weaknesses of cadres in communication and generate communication effectiveness evaluation results;

[0129] The results of the cadre communication effectiveness analysis obtained from the quantitative model are synthesized and evaluated to generate the final communication effectiveness evaluation report. In this step, the communication effectiveness index previously calculated by the formula is used to compare with other data within the organization, such as historical performance records and effectiveness indicators of other cadres at the same level. Through this comparison, the relative position of any cadre's communication effectiveness within the organization can be more accurately located. In addition, the analysis process also includes a reassessment of the influence of each key information to confirm the actual impact of these factors in the current organizational environment and communication requirements. The comprehensive analysis process ensures the practicality and strategic value of the evaluation results, allowing the organization to formulate further human resource strategies and cadre development plans based on detailed data analysis. In this way, the final communication effectiveness evaluation results are not only based on the output of the quantitative model, but also incorporate strategic considerations at the organizational level, providing a comprehensive reference for decision makers.

[0130] See also Figure 8 , the steps to obtain the comprehensive assessment and feedback results are:

[0131] Based on the communication effectiveness evaluation results, combined with work performance and leadership performance, the formula is adopted:

[0132]

[0133] Calculate the cadre comprehensive evaluation index CI to obtain the cadre comprehensive evaluation record, where CEI represents the communication effectiveness index, WP represents the work performance score, and LP represents the leadership performance score;

[0134] Get the specific values ​​CEI = 80, WP = 75, LP = 85. Get based on the daily work evaluation of cadres.

[0135] Calculate the square value:

[0136] CEI 2 =80 2 =6400

[0137] WP 2 =75 2 =5625

[0138] LP 2 =85 2 =7225

[0139] Sum:

[0140] 6400+5625+7225=19250

[0141] Calculate the square root:

[0142]

[0143] Composite Index:

[0144]

[0145] The resulting composite index CI is 46.25, which quantifies the overall performance of the cadre in the three dimensions of communication effectiveness, work performance, and leadership. A high composite index means that the cadre performs well in these three key capabilities. This result shows that the overall ability of this cadre is balanced and high in the assessment, and can be considered as a potential candidate for internal management or leadership roles. Through the composite index, the human resources department can understand the strengths and areas for improvement of each cadre in a more systematic way, providing data support for targeted training and job promotion. The index can also be used as a basis for year-end evaluations and incentives.

[0146] According to the comprehensive evaluation records of cadres, a grading standard is set. If the comprehensive evaluation index of a certain department exceeds the set standard, it will be marked as excellent and the cadre evaluation grade will be obtained;

[0147] When setting the excellent threshold for cadre evaluation, we first export historical performance data from the performance management database, including detailed records of each cadre's communication effectiveness index, work performance, and leadership performance. By analyzing the distribution of these data, we determine the thresholds for different performance levels. For example, the excellent threshold is set as the upper quartile of the comprehensive evaluation index, that is, a value greater than or equal to 75 is excellent, ensuring that only the top cadres can be classified as excellent. If the index is between 60 and 75 points, it is rated as good, and those below 60 points need to receive further training or guidance. This classification method is both scientific and fair, and can dynamically reflect the actual performance of cadres, and provide accurate data support for the human resources department to formulate more effective talent training and development strategies.

[0148] Based on the comprehensive evaluation records and evaluation levels of cadres, they are uploaded to the personal information platform, a feedback mechanism is established, training and improvement suggestions are triggered, and comprehensive assessment and feedback results are generated;

[0149] When the evaluation level and comprehensive evaluation index of the cadres are automatically uploaded to the personal information management system, the system will automatically trigger corresponding training or promotion suggestions based on the data. This mechanism matches the evaluation results of the cadres with the internal training resource library through advanced algorithms to ensure that each cadre can receive personalized development plans according to specific needs. For example, for cadres with lower ratings, the system will recommend basic skills improvement and leadership training, while for cadres with outstanding performance, it will recommend advanced management courses or external learning opportunities. The automated feedback mechanism greatly improves the efficiency and effectiveness of human resource management, allowing each cadre to obtain the most appropriate support and guidance at the most appropriate time, thereby continuously promoting the common growth of organizations and individuals.

[0150] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The cadre assessment system based on big data is characterized by: The system comprises: The data integrity verification module collects cadre assessment data, performs hash processing on the data, calculates and combines the hash values ​​of data nodes, and synchronously records the timestamp and digital signature based on the hash value calculation results to verify the security and traceability of the data and obtain the data integrity status record; The intelligent weight adjustment module extracts the assessment indicators based on the data integrity status record, calculates the correlation between the assessment indicators and the organizational goals, dynamically adjusts the weight settings according to the correlation calculation results, determines the consistency between the assessment standards and the organizational goals, and obtains the adjusted assessment indicator weights; The communication effectiveness analysis module simultaneously analyzes the communication texts of the cadres based on the adjusted assessment index weights, evaluates the emotional tendency and topic logic, extracts key information based on the evaluation results, analyzes the communication effectiveness of the cadres, and generates communication effectiveness evaluation results; The result integration and feedback module conducts a comprehensive evaluation of the cadres based on the communication effectiveness evaluation results, automatically pushes assessment information to the cadres, and establishes comprehensive assessment and feedback results.

2. The cadre assessment system based on big data according to claim 1 is characterized in that: The calculation steps of the data node hash value are: Extract cadres' annual performance data, project contributions and team feedback information, unify data formats, confirm data continuity, and generate original assessment data sets; Based on the original assessment data set, each piece of data is encrypted and converted to verify the security and integrity of the data during processing and transmission, and the encrypted assessment data is obtained; The encrypted assessment data is analyzed using the formula: Calculate the top-level hash value H(T) of the data set and get the data node hash value, where: represents a binary XOR operation, H represents a hash function, and L1 and L2 represent the hash values ​​of any two adjacent data nodes.

3. The cadre assessment system based on big data according to claim 2 is characterized in that: The steps for obtaining the data integrity status record are: Based on the hash value of the data node, the current timestamp is synchronously recorded and digitally signed to confirm the immutability of the data and generate signature data with a timestamp; Perform integrity verification on the signature data with the timestamp, use the public key to confirm that the data has not been tampered with from the time of signing to the present, verify the authenticity and integrity of the data, and obtain the verified signature data; According to the verified signature data, the signature verification time and status of each data node are recorded, the integrity and security status of the data are confirmed, and a data integrity status record is generated.

4. The cadre assessment system based on big data according to claim 3 is characterized in that: The calculation steps of the correlation degree are: Based on the data integrity status record, perform data query and screening, extract key assessment indicators, and generate preliminary screening results of assessment indicators; According to the preliminary screening results of the assessment indicators, the occurrence frequency and importance of the assessment indicators are analyzed, and the first-order correlation coefficient between each assessment indicator and the organizational goal is calculated to obtain the preliminary correlation analysis results between the assessment indicators and the organizational goal; According to the preliminary correlation analysis results between the assessment indicators and organizational goals, the formula is adopted: Calculate the correlation R between the i-th assessment indicator and the organizational goal i , get the correlation analysis record between the assessment indicators and the organizational goals, where p ij is the first-order correlation coefficient of assessment indicator i to organizational goal j, o j represents the operational complexity of organizational goal j, and n is the total number of organizational goals.

5. The cadre assessment system based on big data according to claim 4 is characterized in that: The steps for obtaining the adjusted assessment index weights are as follows: Based on the correlation analysis records between the assessment indicators and the organizational goals, preliminarily adjust the weight of each assessment indicator to maximize the probability of achieving the organizational goals and generate a preliminarily adjusted weight configuration; Apply the weight configuration after the preliminary adjustment to conduct sensitivity analysis, confirm the impact of the new weight setting on the achievement of the target through simulation analysis, compare the performance impact before and after the indicator weight adjustment, and generate the weight adjustment impact analysis results; Based on the results of the weight adjustment impact analysis, the effects of the weight adjustments of all assessment indicators are revealed, aligned with the organizational goals, and the adjusted assessment indicator weights are generated.

6. The cadre assessment system based on big data according to claim 5 is characterized in that: The steps for evaluating the sentiment tendency and topic logic are as follows: Using the adjusted assessment indicator weights, comprehensively analyze the cadre communication texts, extract key words and phrases from the texts, and generate preliminary text analysis results; Based on the preliminary text analysis results, sentiment analysis is performed using the formula: Calculate the sentiment tendency score E of the cadre communication text and generate the sentiment tendency evaluation result, where Q represents the positive sentiment score and N represents the negative sentiment score; Combined with the sentiment tendency assessment results, text logic analysis is conducted to evaluate the thematic logic of the cadre communication text, confirm the consistency of the content with organizational goals, and generate comprehensive assessment results.

7. The cadre assessment system based on big data according to claim 6 is characterized in that: The steps for obtaining the communication effectiveness evaluation results are as follows: Based on the comprehensive evaluation results, extract key information, identify key factors affecting communication effectiveness, and generate key factor analysis results; According to the results of the key factor analysis, the communication effectiveness of cadres is analyzed using the formula: Calculate the communication effectiveness index E eff , generate the results of cadre communication effectiveness analysis, where I i represents the standardized value of key information, and m is the number of key information; Based on the results of the cadre communication effectiveness analysis, combined with the communication frequency and the frequency of occurrence of key information, and compared with the average performance within the organization, the strengths and weaknesses of cadres in communication are identified, and communication effectiveness evaluation results are generated.

8. The cadre assessment system based on big data according to claim 7 is characterized in that: The steps for obtaining the comprehensive assessment and feedback results are as follows: Based on the communication effectiveness evaluation results, combined with work performance and leadership performance, the formula is used: Calculate the cadre comprehensive evaluation index CI to obtain the cadre comprehensive evaluation record, where CEI represents the communication effectiveness index, WP represents the work performance score, and LP represents the leadership performance score; According to the comprehensive evaluation records of the cadres, a grading standard is set. If the comprehensive evaluation index of a certain department exceeds the set standard, it is marked as excellent and the cadre evaluation grade is obtained; Based on the comprehensive evaluation records of the cadres and the evaluation levels of the cadres, they are uploaded to the personal information platform, a feedback mechanism is established, training and improvement suggestions are triggered, and comprehensive assessment and feedback results are generated.

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